On the Approximate Weak Chebyshev Greedy Algorithm in Uniformly Smooth Banach Spaces
This work provides theoretical guarantees for a more practical variant of the WCGA, enabling its use in numerical applications where exact computations are infeasible.
The paper studies the Approximate Weak Chebyshev Greedy Algorithm (AWCGA) in uniformly smooth Banach spaces, establishing necessary and sufficient conditions for its convergence. It shows that when perturbations and errors are from ℓ₁ space, convergence conditions match those of the WCGA, and under specific choices, the AWCGA achieves the same convergence rate as the WCGA for elements in the closure of the convex hull of the dictionary.
We study greedy approximation in uniformly smooth Banach spaces. The Weak Chebyshev Greedy Algorithm (WCGA) is defined for any Banach space $X$ and a dictionary $\mathcal{D}$, and provides nonlinear $n$-term approximation with respect to $\mathcal{D}$. In this paper we study the Approximate Weak Chebyshev Greedy Algorithm (AWCGA) -- a modification of the WCGA that was introduces by V.N. Temlyakov. In the AWCGA we are allowed to calculate $n$-term approximation with a perturbation in computing the norming functional and a relative error in calculating the approximant. Such permission is natural for the numerical applications and simplifies realization of the algorithm. We obtain conditions that are necessary and sufficient for the convergence of the AWCGA for any element of $X$. In particular, we show that if perturbations and errors are from $\ell_1$ space then the conditions for the convergence of the AWCGA are the same as for the WCGA. For specifically chosen perturbations and errors we estimate the rate of convergence for any element $f$ from the closure of the convex hull of $\mathcal{D}$ and demonstrate that in special cases the AWCGA performs as well as the WCGA.